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Record W4323810113 · doi:10.1097/md.0000000000033153

Risk factors for post-extubation dysphagia in ICU: A systematic review and meta-analysis

2023· review· en· W4323810113 on OpenAlexaboutno aff
Lingyu Hou, Ying Li, Jianhua Wang, Yuqi Wang, Jingchao Wang, GuoJing Hu, Xiao Rong Ding

Bibliographic record

VenueMedicine · 2023
Typereview
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsnot available
FundersSanming Project of Medicine in Shenzhen
KeywordsMedicineCochrane LibraryDysphagiaInclusion and exclusion criteriaIntensive care unitMeta-analysisOdds ratioIntubationSwallowingData extractionMEDLINEIntensive care medicineTracheal intubationEmergency medicineInternal medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Post-extubation dysphagia is high in critically ill patients and is not easily recognized. This study aimed to identify risk factors for acquired swallowing disorders in the intensive care unit (ICU). METHODS: We have retrieved all relevant research published before August 2022 from PubMed, Embase, Web of Science, and the Cochrane Library electronic databases. The studies were selected using inclusion and exclusion criteria. Two reviewers screened studies, extracted data, and independently evaluated the risk of bias. The quality of the study was assessed with the Newcastle-Ottawa Scale, and a meta-analysis was carried out with Cochrane Collaboration's Revman 5.3 software. RESULTS: A total of 15 studies were included. Age (odds ratio [OR] = 1.04), tracheal intubation time (OR = 1.61), APACHE II (OR = 1.04), and tracheostomy (OR = 3.75) were significant risk factors that contributed to post-extubation dysphagia in ICU. CONCLUSION: This study provides preliminary evidence that post-extraction dysphagia in ICU is associated with factors such as age, tracheal intubation time, APACHE II, and tracheostomy. The results of this research may improve clinician awareness, risk stratification, and prevention of post-extraction dysphagia in the ICU.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.815
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0110.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.286
GPT teacher head0.535
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2023
Admission routes1
Has abstractyes

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